New Sliding Mode Control Based on Tracking Differentiator and RBF Neural Network

Abstract

In order to solve the problem that the control system of permanent magnet synchronous motor (PMSM) is difficult to meet the high control accuracy due to the influence of non-repeated disturbances such as external disturbance, system parameter variation, and friction force during operation, a novel sliding mode control (NSMC) method based on tracking differentiator (TD) and radial basis (RBF) neural network was proposed. Firstly, a new sliding mode reaching law is proposed by adding the state variables to the traditional exponential reaching law, which can effectively reduce the chattering of the system. Then, the speediest tracking differentiator is designed to estimate the given speed signal and its differential signal, to realize the novel sliding mode variable structure algorithm. Finally, the RBF neural network is used to compensate for the uncertainty and external interference of the system; the robustness of the system is further improved by adaptive weight updating. The simulation results show that, by comparing with the traditional exponential approach law, the overshoot of 22 r/min is reduced by the control method based on the new hybrid reaching law, the speed decrease amplitude is reduced by 77.1% under load disturbance, and the speed recovery time is shortened by 0.059 s. After the optimization of the new sliding mode control method based on tracking differentiator and RBF neural network, the overshoot of 86 r/min is further reduced, the speed decrease amplitude of load disturbance is reduced again by 48.5%, and the speed recovery time is shortened again by 0.073 s.

Dynamic Temperature Compensation of Pressure Sensors in Migratory Bird Biologging Applications

“…Radial basis neural networks map low-dimensional data inputs to higher dimensions by means of radial basis functions, thus transforming a problem that is not linearly differentiable in low-dimensional space into a linearly differentiable problem in high-dimensional space [30]. It has been proven that on a compact set, the radial basis neural network can approximate any nonlinear function with arbitrary accuracy [31]. Compared with other feed-forward neural network models, the radial basis neural network has the advantages of simple structure, fast training speed, and strong generalizability.…”

Strict Sliding Mode Control with Power Reaching Law and Disturbance Bounds in Synchronous Servo Tracking Drive System

“…The sliding-surface power reaching law and exponential reaching law was used in several studies [1,3], [20−24]. In a traditional sliding mode controller with exponential reaching law, fast reaching and low chattering cannot be considered simultaneously.…”

“…It facilitates the control law of the outer loop by directtorque control through a DC voltage input. With this object, the outer loop sliding control law is synthesized based on the combination of constant sliding-surface reaching speed and exponential reaching speed [2,3], [20−24]. The disturbance compensation control component is quantified based on the upper bound and lower bound of the disturbance, so the constant slidingsurface reaching speed is strictly confined.…”

Brain Emotional Learning Control Based on Radial Basis Function for Permanent Magnet Synchronous Motor

“…Motor operation requires a motor controller. A PMSM speedcontrol system adopts PI, direct torque, model prediction, sliding model [4][5][6][7][8], and other intelligent controls. PI control is a common control method in a PMSM speed-regulating system.…”